Businesses can now use conversational AI to automate customer-facing touchpoints everywhere — on social media platforms like Facebook and Twitter, on their website, their app or on voice assistant devices. Industry giants like Apple, Amazon, Baidu, Facebook, Google, IBM and Microsoft are investing large resources to drive AI progress. And though it’s still relatively new among enterprises, by 2021 Gartner predicts 25% of enterprises across the globe will have a virtual assistant to handle support issues.
If your organization is not yet familiar with conversational artificial intelligence, it is a set of technologies that enable computers to simulate real conversations. According to Georgia Partners, conversational AI refers to the use of messaging apps, speech-based assistants and chatbots to automate communication and create personalized customer experiences at scale. The popularity of voice assistants like Amazon Alexa, Google Home, Suri and voice activation in cars has pushed this nascent technology into the spotlight. And its popularity is predicted to only grow: Market Research Future estimates that by 2023, the voice assistant market will be worth $7.8 billion.
The most common use case for conversational AI is consumers interacting via chatbots to deal with a customer service request, such as returning a product or move funds between accounts. In fact, analysts at Forrester report that 45% of end users prefer chatbots to other forms of communication for customer service inquiries. For call center departments, using conversational AI is a smart way to reduce operational costs while having the ability to scale quickly during peak periods.
Conversational AI Is Still Immature
However, current chatbots are not very intelligent and only are able to handle fairly rudimentary requests. Their limited access to knowledge is stunting their ability to conduct more meaningful conversations that help customers quickly and accurately complete their intended transaction. For the most part, chatbot conversations are immature, built based on a decision-tree logic, where the response given by the bot depends on specific keywords identified in the user's input. For example:
IF user's input contains 'shop' or 'buy';
THEN send message with product list
In the meantime, companies eager to successfully deploy a chatbot end up with a bunch of bots that address irrelevant use cases, or offer really poor experiences. A quick search of “chatbot fails” brings up epic examples.
Except in cases where bots are powered by natural language processing technology, they can't hold contextual information for longer than a few chat bubbles, and end up getting lost in the conversation.
Related Article: A Good Chatbot Is Hard to Find
Conversations Need a Connection
Automating and scaling one-to-one intelligent conversations appeals to many organizations in a variety of industries. In making this happen, developers play an important role at defining how each conversation is scripted and the behaviors users can expect when interacting with bots.
But, despite the best of intentions, chatbots sometimes fail to deliver experiences that are seamless and efficient. As a result, a recent study by Clutch and Ciklum found 70% of consumers are unlikely to trust an AI-powered voice assistant to correctly answer simple messages, or even make simple calls for them. Data accuracy is a huge concern.
A key factor limiting quick access to accurate information is the difficulty developers have connecting back end systems of record that are ready with meaningful, actionable content with inquires and commands.
Fortunately, connecting systems of records with systems of engagement processes is where RPA has been used successfully. Initially, RPA had been used mainly for automating basic repeatable administrative tasks (what is often referred to “swivel chair” data entry tasks) within the enterprise. But within the past two years, RPA has fostered a new digital workforce that has integrated with intelligent capture and expanded to automate all business processes to derive meaning and understanding from content. This same concept can be applied to enable conversational AI by using RPA to connect with chatbots.
RPA addresses the expectations consumers have with customer support inquires. According to Microsoft’s 2018 State of Global Customer Experience Report, more than 75% of respondents expect customer service representatives to have visibility into previous interactions and purchases. Yet nearly half of those surveyed said agents almost never or only occasionally have the context they need to most effectively and efficiently solve their issue. Since the majority of conversational AI is handling customer interactions, RPA is the ideal tool to connect business processes and deliver meaningful data quickly to chatbot platforms.
For example, some banks offer the ability to check balance of a checking account via a chatbot. With RPA, banks could expand this capability to more sophisticated inquires such as check status of a loan.
Related Article: Combine Chatbots and RPA Bots for Better Customer Service
Ramp Up Your Conversational AI
MarketsandMarkets forecasts the conversational AI market will reach $15.7 billion by 2024. It bases this bold estimation on the increasing demand for AI-powered customer support services, omnichannel deployment, and reduced chatbot development costs. So how can your organization ramp up its use of conversational AI, and what uses cases will deliver a pleasant, yet meaningful experience?
Learning Opportunities
Webinar
Mar
29
The CX Commute vs. the CX Joy Ride
Is your CX traffic holding up your customer's journey?
Webinar
Mar
29
Is Your Microsoft 365 Data Recoverable? How to Survive the IT Hotseat When Data is Lost
See why MIcrosoft recommends third party backup
Webinar
Mar
30
An Optimized Customer Contact Strategy Combines Transparency and Intelligence: The State of Outbound Communications
Learn from the Neustar-commissioned Forrester Consulting study on challenges and solutions for contact centers
Webinar
Apr
5
Adopt a Modern Digital-First Marketing Strategy
Learn how to use a composable DXP to your advantage
Make conversational AI bots smarter
As mentioned previously, most chatbots are designed on decision-tree logic consisting of structured content. To train chatbots to converse like humans, rather than humans trying to communicate like a computer, the ability to process unstructured content becomes vital. Organizations are using content intelligence, a combination of OCR, machine learning and other AI technology to create structure information from unstructured content. This helps make RPA bots smarter with cognitive skills that deliver human-like understanding of content, and to connect those skills with chatbots.
RPA is an easy way to connect chatbots to systems to perform simple tasks, but when combined with content intelligence, it can facilitate the handling of more complex requests. So now, the request for status of loan can waterfall into a number of inquiries and commands, such as requesting and processing additional loan documentation from the customer.
Related Article: Calling All Linguists: The Messaging Bots Need Help
Always think mobile
Customers expect brands to be available on multiple channels, with phone or other voice channels being among the most frequently preferred according to Microsoft’s report. Mobile devices make up 42% of total time spent online. Additionally, two-thirds of customers prefer to first try solving issues on their own, thereby making self-service a cornerstone of any omnichannel strategy.
Advanced mobile devices today are flexible to address consumers’ various preferences: phone call, chatbot, email, social media — with the added bonus of access to a camera.
Cameras are a powerful tool for organizations and customers alike. They enable customers to snap a photo of supporting documentation and submit via the desired channel. Customers can interact with a bank or insurance company by sending proof of ID or photos of a damaged car all of which represents content that power business processes that customers rely on.
An example of an omnichannel strategy is submitting an online application for a line-of-credit with your smartphone. As part of the initial loan request, you may snap photos with your phone of a few documents that show proof of identity, proof of residency, and proof of employment. After initiating the loan process, a consumer might follow up with the bank to check status of the loan via an online chatbot. That conversation could also result in requesting the customer send additional documentation. In this example, content intelligence starts the process and is also used later in the process.
Related Article: Conversational AI Needs Conversation Design
Continuously learn from interactions
Interactions with an AI-driven chatbot are also excellent at revealing customers’ needs. The customer experience is enhanced by collecting actionable detail like pain points and gauging the popularity of services.
Conversational AI is an emerging channel for businesses to better connect with customers, partners and employees. To make the conversations more intelligent, the combination of RPA and content intelligence is a smart way to leverage your content with existing business processes.
Learn how you can join our contributor community.
